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In this work, we propose and develop deconvolution architecture for efficient FPGA implementation. FPGA-based accelerators are proposed for both deconvolution ...

Optimizing CNN-based Segmentation with Deeply Customized ...

Optimizing. CNN-based Segmentation with Deeply Customized Convolutional and Deconvolutional Architectures on FPGA. ACM Trans. Reconfig. Technol. Syst. 1, 1 ...

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ing CNN-based Segmentation with Deeply Customized Convolutional and Deconvolutional Architectures on. FPGA. ACM Trans. Reconfigurable Technol. Syst. 11, 3 ...

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Optimizing CNN-based Segmentation with Deeply Customized Convolutional and Deconvolutional Architectures on FPGA · Shuanglong Liu, Hongxiang Fan, +3 authors. W.

Optimizing CNN-based Segmentation with Deeply Customized ...

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Optimizing CNN-based segmentation with deeply customized ...

Optimizing CNN-based segmentation with deeply customized convolutional and deconvolutional architectures on FPGA ... optimization techniques. A non-linear ...

Optimizing CNN-based Segmentation with Deeply Customized ... - dblp

Bibliographic details on Optimizing CNN-based Segmentation with Deeply Customized Convolutional and Deconvolutional Architectures on FPGA.

accessible and customizable deep-learning image segmentation

... deep neural networks, many pieces of the process become daunting; optimizing the many user-defined “hyper-parameters” of the algorithm ...

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Furthermore, the currently available dentomaxillofacial segmentation software programs have been optimized based on CT data, which cannot be applied to CBCT ...

A Novel Approach to Optimizing Convolutional Neural Networks for ...

When it comes to image segmentation, the fundamental idea behind deep learning is to train a convolutional neural network (CNN) to learn a ...

Image Segmentation: Architectures, Losses, Datasets, and ...

al 2017 “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation” | Source ... """ # configuration name NAME = "customized ...

Convolutional Neural Networks (CNNs): A 2025 Deep Dive - viso.ai

Recent innovations in CNN design focus on optimizing network ... U-Net, a CNN architecture for biomedical image segmentation, is a prime example.

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... optimization algorithm for learning deep CNN applied to MRI segmentation ... Ali et al. Where should I go? A deep learning approach to personalize type-based ...

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The pre-processed picture is next subjected to entropy-based segmentation algorithms, which separate the image's significant areas in order to distinguish ...

‪Fan Hongxiang‬ - ‪Google Scholar‬

Optimizing CNN-based segmentation with deeply customized convolutional and deconvolutional architectures on FPGA. S Liu, H Fan, X Niu, H Ng, Y Chu, W Luk. ACM ...

An Efficient and Optimal Deep Learning Architecture using Custom ...

In this paper, It have proposed a kidney tumor semantic segmentation model based on CU-Net and Mask R-CNN to extract kidney tumor information from abdominal MR ...

Optimizing Convolutional Neural Network Architecture - arXiv

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Convolutional Neural Networks (CNN) and Deep Learning - Intel

Developing and deploying a CNN model is a complex process with three stages: training, optimizing, and inference. Computer vision combines hardware and software ...

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Customized designs can be optimized, FPGA. Timing latency, Implemented FPGA ... Deep learning-based image segmentation on multimodal medical imaging. IEEE ...

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Accelerating biomedical image segmentation using equilibrium optimization with a deep learning approach. ... The results were promising in many cases, and custom ...